Project will focus on treatment, rather than discovery.

NEW YORK—Earlier today, IBM announced that it would be using Watson, the system that famously wiped the floor with human Jeopardy champions, to tackle a somewhat more significant problem: choosing treatments for cancer. In the process, the company hopes to help usher in the promised era of personalized medicine.

The announcement was made at the headquarters of IBM's partner in this effort, the New York Genome Center; its CEO, Robert Darnell called the program "not purely clinical and not purely research." Rather than seeking to gather new data about the mutations that drive cancer, the effort will attempt to determine if Watson can parse genome data and use it to recommend treatments.

Darnell said that the project would start with 20 to 25 patients who are suffering from glioblastoma, a type of brain cancer with a poor prognosis. Currently, the median survival time after diagnosis is only 14 months; "Time, frankly, is not your friend when you have glioblastoma," as Darnell put it. Samples from those patients (including both healthy and cancerous tissue) would be subjected to extensive DNA sequencing, including both the genome and the RNA transcribed from it. "What comes out is an absolute gusher of information," he said.

It should theoretically be possible to analyze that data and use it to customize a treatment that targets the specific mutations present in tumor cells. But right now, doing so requires a squad of highly trained geneticists, genomics experts, and clinicians. It's a situation that Darnell said simply can't scale to handle the patients with glioblastoma, much less other cancers.

Instead, that gusher of information is going to be pointed at Watson. John Kelly of IBM Research stepped up to describe Watson as a "cognitive system," one that "mimics the capabilities of the human mind—some, but not all [capabilities]." The capabilities it does have include ingesting large volumes of information, identifying the information that's relevant, and then learning from the results of its use. Kelley was extremely optimistic that Watson could bring new insights to cancer care. "We will have an impact on cancer and these other horrific diseases," he told the audience. "It's not a matter of if, it's a matter of when—and the when is going to be very soon."

IBM's Ajay Royyuru points to a drawing of the chemical formula for DNA at IBM Research headquarters in Yorktown Heights, New York.

Teaching a computer biology

Kelly noted that IBM shifted Watson to a cloud-based system (it was originally run on a dedicated cluster), which means that any success of the initial endeavor would be easier to use elsewhere. Perhaps more significantly, however, Kelly said "We had to teach Watson the language of healthcare and medicine." How does one go about teaching a computer cancer biology?

We talked a bit to IBM's Ajay Royyuru about the process. To get the machine started, Royyuru said they took advantage of the fact that the National Institutes of Health has compiled lists of biochemical pathways—signaling networks and protein interactions—and placed them in machine-readable formats. Once those were imported, Watson's text analysis abilities were set loose on the NIH's PubMed database, which contains abstracts of nearly every paper published in peer-reviewed biomedical journals.

Over time, Watson will develop its own sense of what sources it looks at are consistently reliable. Royyuru told Ars that, if the team decides to, it can start adding the full text of articles and branch out to other information sources. Between the known pathways and the scientific literature, however, IBM seems to think that Watson has a good grip on what typically goes on inside cells.

How is it going to use that knowledge? So far, most of the cancer genome sequencing efforts have focused simply on identifying which mutations are likely to be the key to driving uncontrolled cell growth. This effort has reinforced the notion that there are a handful of core genes that trigger a variety of cancers, along with a larger array that are either involved in specific cell types or in promoting specific aspects of cancerous growth like invasiveness. (Although Darnell was quick to point out that discoveries in this area are ongoing.)

In goes data, out comes a treatment

Ultimately, Watson's not going to be used for that. Instead, it's going to be used to help design treatments. Given the results of the DNA and RNA sequencing—the geyser Darnell mentioned earlier—Watson will figure out which mutations are distinct to the tumor, what protein networks they affect, and which drugs target proteins that are part of those networks. The net result will be a picture of the biochemical landscape inside the tumor cells, along with some suggestions on how clinicians might consider intervening to change the landscape.

Royyuru told Ars that Watson will be aware of two key concepts. One is that these signaling networks often have redundancies—if you block one protein with a drug, then others in the network will make up for its absence. To avoid this, Watson can suggest combinations of drugs that target multiple arms of the network. The other thing we've found is that tumors are a population of different cells, not all of which have the same combination of mutations. This heterogeneity can be identified in the sequencing results, and Watson should be able to pick drugs that can target distinct populations within the tumor.

Ultimately, Watson won't do more than make a set of suggestions and provide a convenient interface for clinicians to explore the data that led to those suggestions. (As Royyuru's colleague Raminderpal Singh told me, "You don't want to be making the decisions, because then you're in the medical business.") But, as Darnell noted in his introduction, right now those recommendations take a team of specialized researchers about a week to make. Watson can generate them almost as soon as the sequencing data is ready.

And that, everyone hopes, will be the start of our ability to tailor treatments to the genetic changes that make each case of cancer a distinctive medical challenge. A panel of 25 patients won't be enough to really know how effective this is (though Watson will ask the clinicians why they chose specific treatments and add that information to the data it considers in the future). But the program, which has already gotten approval to enroll human patients, involves a partnership with all the major New York City hospitals, as well as one each in Long Island and Buffalo. If the first results are promising, the program could be expanded rapidly.

Watson's text analysis abilities were set loose on the NIH's PubMed database, which contains abstracts of nearly every paper published in peer-reviewed biomedical journals.

Over time, Watson will develop its own sense of what sources it looks at are consistently reliable. Royyuru told Ars that, if the team decides to, it can start adding the full text of articles and branch out to other information sources. Between the known pathways and the scientific literature, however, IBM seems to think that Watson has a good grip on what typically goes on inside cells.

Wow, this reads like something out of an early Asimov story. I'm seriously impressed.

Its not likely that any of these IBM executives understand much about the technical details of either the computer of the biology sides of these efforts. Nor is there anything new about dreams either about cancer cures or what AI could do. On the other hand there is no doubt that computational biology is already a large success story. Nor is there any doubt that computer models of system biology will be extremely important to biology's future in the coming decades. Whether this success will be dependent on particularly powerful computers and methods that only those computers understand or whether it will depend primarily on human intelligence in effectively using the ordinary computers that everyone has access to remains to be seen. I suspect the human intelligence will be more important. But, it is obvious that there are already a large number of very intelligent scientists focusing on this kind of problem. In any case, having ever more genetic data is critical. I expect the day is coming in the not too distant future when every doctor will be part of this research effort. Genetic sequencing will be standard. Doctors will be evaluating the relationship between phenotypes and genes. AI techniques will be used to capture the results of the most successful diagnoses and at least assist all doctors in their work. Whether Watson can speed this kind of process up remains to be seen. So does an answer to the question whether a big difference can be made in cancer treatment without a qualitative breakthrough to new kinds of therapeutic agents.

We aren't there yet, but it seems that it's only a matter of time before protein structure can be reliably predicted from mRNA sequence. Once we're doing that, just one more leap will see us designing customized molecules to target just those structural abnormalities (or just those mutated mRNA strands). Aptamers, for example, would do the job nicely.

Of particular note is the "Genomics of Drug Sensitivity in Cancer"http://www.cancerrxgene.orgthey (and others) are trying to correlate tumor cell genotype with which drugs do and do not work with a few notable successes so far

As someone that has watched their kid get wrecked by cancer and the resulting treatment, I keep hoping there is something better than chemo, but that seems a ways off. I hope the results of this can help focus current treatments better, especially in rarer cancers that have very little research $$.

We aren't there yet, but it seems that it's only a matter of time before protein structure can be reliably predicted from mRNA sequence. Once we're doing that, just one more leap will see us designing customized molecules to target just those structural abnormalities (or just those mutated mRNA strands). Aptamers, for example, would do the job nicely.

To expand on your post a little bit: there are hosts of post translational modifications that proteins go through after synthesis that all can change the final sequence. Examples include adding various sugar molecules to various parts of a protein and/or linking the protein into a larger complex with several other proteins to alter the conformation, hydrophilicity, etc. While we can infer structure from the mRNA sequence, there are a multitude of additional factors (complex proteins, cofactors, pH, etc) that effect the final shape, and therefor function of a protein.

Additionally, we can already target specific RNA sequences: http://en.wikipedia.org/wiki/Small_interfering_RNAThe problem with using siRNAs is delivery and how long they stick around. Simply put, it's not terribly easy to get a drug (protein, molecule or nucleotide sequence) into a cell since you have to survive the stomach/digestion process, travel to the target site(s), somehow cross the plasma membrane into the cell all while avoiding immune responses.

I'm actually currently reading The Emperor of All Maladies, which is a history of oncology, and it's fascinating. One thing it highlights is that there is no "magic bullet" for cancer, and that there've been a few claims of a definitive cure* in the past century, none of which panned out. That said, this particular approach also acknowledges just how diverse cancers are and is something like the opposite of a magic bullet cure, which makes me rather more confident than I might otherwise be about its success.

*Radical surgery and bone-marrow grafting are the big ones so far. The first aimed to cut out a tumour and all the surrounding tissue to prevent cancer from ever recurring. Didn't work, as cancers can enter the bloodstream and reoccur elsewhere. The latter allowed for higher-dose chemotherapy (bone marrow is the tissue most sensitive to chemo in the human body) and the hope was that if you just pumped someone full of enough toxins you'd have to kill all the cancer. It didn't pan out either.

Watson's text analysis abilities were set loose on the NIH's PubMed database, which contains abstracts of nearly every paper published in peer-reviewed biomedical journals.

Over time, Watson will develop its own sense of what sources it looks at are consistently reliable. Royyuru told Ars that, if the team decides to, it can start adding the full text of articles and branch out to other information sources. Between the known pathways and the scientific literature, however, IBM seems to think that Watson has a good grip on what typically goes on inside cells.

Wow, this reads like something out of an early Asimov story. I'm seriously impressed.

Sometimes I get the feeling that a huge revolution in AI (and to a lesser extent, robotics) is just around the corner. That I am going to wake up one day and there will be some historic event that really catches everyone by surprise. But just as often, it seems like the promise of radical progress due to AI is always just beyond the horizon, and it feels like nothing has really changed over the last few decades.

Certainly to some extent, Watson winning at Jeopardy (and Deep Blue at chess, back in the day) was impressive and got a lot of attention, but it sort of feels like nothing really happened afterwards. That Jeopardy match was over 3 years ago. Since then I've been hearing about how Watson might be useful for this or that, but I haven't actually heard of any results.

So this latest story is cool, and it would be great if Watson could make a big difference, but I get the feeling that it will be at least a couple of years before we hear of any results, and they will probably be along the lines of "Watson seems to have helped a bit (probably!)".

I just wish that one day I will be browsing Google News and the top story will be "Super AI cures cancer!" or something. I know that is probably too simplistic but I feel like it has been a long time since technology has really impressed me.

Since around 2000 I feel like all of the major progress has been in technology I don't care about, like social networks and smartphones. So please, Watson, do something inspiring already!

But I guess there's at least VR (Oculus Rift) to look forward to!

EDIT: Maybe this is a naive view, but I am still hoping that in the case of AI, there is some threshold that once passed will usher in some really incredible results. AI has seemed disappointing (by the public at least) for decades now, but I hold out hope that it will soon get just good enough to really impress. Referring again to VR, a lot of effort has gone into that field for decades, but only recently with the Oculus Rift has it gotten good enough to be considered "revolutionary". I guess I've just been hoping that Watson is at that same level ("wow, it won at Jeopardy!") but maybe it's not quite there yet.

I forgot to add, I think autonomous cars are also extremely promising. I am hopeful that we will see a revolutionary consumer version within the next 5 years.

As I understand it, Watson excels at natural language processing. But here they're talking about chemical pathway analysis which appears to be a different type of reasoning. (Though they did mention that it will also scour research article abstracts from PubMed.) Can someone with more insight chime in?

Probably. Watson won't discriminate treatments based on politics or morals, unless instructed to do so. It won't forget things. It will have instant access to the latest research, and will be able to verify or disprove said research when it's put to use.

Watson really is the next frontier in computing. I'm grateful to know it's being put to use to help solve, and hopefully one day cure, cancer.

dnjake : IBM Research is, oddly enough a pure, research organisation, with actual scientists working on actual science, i.e., a good number of them are spending time working in labs, and Life Sciences makes up a big part of that right now. So you might be surprised by how much these IBM Executives actually know about it.

Eh, I'll hope for the best but I doubt this is going to be terribly fruitful. Having sifted through some of this very data for projects before, I can say that there is a tremendous amount of marginal information contained in it (that's just the nature of some of the experiments used to produce the data). Oh, sure, pathways are mostly nailed down in the context of how we think most of them function, but inter-pathway interactions are very difficult due to a lack of ability to model them without experimental data. People have already been applying advanced machine learning techniques to all of this data, and the problems/limitations encountered aren't due to computational limits. That said, a lot of good work has come from these efforts.

I'd love to know more about the details though. Given Watson (to my understanding) is mostly a natural language interface coupled to some sort of learning/scoring algorithm generator (albeit a very, very good one), it's probably just going to be doing the same thing that researchers have already been doing, except with a lot more people, funding, and publicity.

I also find it interesting to ponder whether or not a super smart hard AI--or augmented person--would actually be capable of performing significantly better (not counting speed) in fields which are still data limited (or so we think) compared to ordinary people. Could a super AI take existing particle physics data and produce relevant new theories? It doesn't matter how smart you are if you are completely deprived of information to form hypotheses with. Not saying that's what's going on with biology. If we could just figure out protein folding and interactions with simulations alone on a reasonable time scale...

"We will have an impact on cancer and these other horrific diseases," he told the audience. "It's not a matter of if, it's a matter of when—and the when is going to be very soon"

If there are two things for which we have a bad track record in delivering, it's AI and curing cancer. So, regardless of the fact that we should try all types of strategies and that this may well be a promising approach, these sweeping statements do not help. Working in the field of systems approaches for medicine, they actually embarrass me a bit.

Sometimes I get the feeling that a huge revolution in AI (and to a lesser extent, robotics) is just around the corner. That I am going to wake up one day and there will be some historic event that really catches everyone by surprise. But just as often, it seems like the promise of radical progress due to AI is always just beyond the horizon, and it feels like nothing has really changed over the last few decades.

I don;t think what we think of as 'strong AI' is going to arrive in a sudden revolutionary achievement. It's going to come from the slow plodding advance of computing, quietly and inexorably. In the last 10 years we have gone from carrying around a device that can send audio and text to other people, to walking around with a device that can access the global collection of human knowledge, and with the aid of massive remote computational system can answer a spoken question with a likely relevant answer. Nobody has really made a song-and-dance about Google Now, and Siri got maybe 6 months of advertising prominence before fading to 'all our stuff does this too' status along with 'having a camera' or 'being able to locate your position anywhere on the planet within a handful of metres'. We accumulate bigger and bigger datasets, point more powerful heuristic systems at them, and provide friendlier and more 'natural' interfaces with them. Eventually we're going to go 'wait, do we need to ascribe some rights to these things?', and after a few rounds of that one of them is going to answer 'yes'.

I'd love to know more about the details though. Given Watson (to my understanding) is mostly a natural language interface coupled to some sort of learning/scoring algorithm generator (albeit a very, very good one), it's probably just going to be doing the same thing that researchers have already been doing, except with a lot more people, funding, and publicity.

Isn't that what the article says...? It will be producing an analysis that currently takes a team of guys a week, but in less time...

Quote:

Ultimately, Watson won't do more than make a set of suggestions and provide a convenient interface for clinicians to explore the data that led to those suggestions. (As Royyuru's colleague Raminderpal Singh told me, "You don't want to be making the decisions, because then you're in the medical business.") But, as Darnell noted in his introduction, right now those recommendation take a team of specialized researchers about a week to make. Watson can generate them almost as soon as the sequencing data is ready.

This is really cool, but I think that it's unfortunate that we spend orders-of-magnitude less for cancer prevention. And many of the cancer-prevention techniques- healthy diet, exercise etc. etc. also helps prevent heart disease, which is another expensive killer.

This is really cool, but I think that it's unfortunate that we spend orders-of-magnitude less for cancer prevention. And many of the cancer-prevention techniques- healthy diet, exercise etc. etc. also helps prevent heart disease, which is another expensive killer.

The article was about (a specific type of) brain cancer. I don't know that we have any cancer prevention techniques for brain cancers of any kind.

The problem with the prevention hoopla is that it's victim blaming, and sick shaming.

EDIT: Maybe this is a naive view, but I am still hoping that in the case of AI, there is some threshold that once passed will usher in some really incredible results. AI has seemed disappointing (by the public at least) for decades now, but I hold out hope that it will soon get just good enough to really impress. Referring again to VR, a lot of effort has gone into that field for decades, but only recently with the Oculus Rift has it gotten good enough to be considered "revolutionary". I guess I've just been hoping that Watson is at that same level ("wow, it won at Jeopardy!") but maybe it's not quite there yet.

This reminded me of days spent playing a crazy Russian scientist on a planet four light years away.

Quote:

There are two kinds of scientific progress: the methodical experimentation and categorization which gradually extend the boundaries of knowledge, and the revolutionary leap of genius which redefines and transcends those boundaries. Acknowledging our debt to the former, we yearn, nonetheless, for the latter.

Probably. Watson won't discriminate treatments based on politics or morals, unless instructed to do so. It won't forget things. It will have instant access to the latest research, and will be able to verify or disprove said research when it's put to use.

"Listen, and understand. Watson is out there. It can't be bargained with. It can't be reasoned with. It doesn't feel pity, or remorse, or fear. And it absolutely will not stop, ever, until you are cured of cancer."

Seriously though, this is fantastic work. The sheer volume of information from medical research is so vast that it's not realistic to expect humans to be able to sift through it in most cases. Hopefully AIs can be used, not just in tackling cancer, but in putting up to date information into the hands of all doctors to augment their personal experience and learning in a way that would be impossible through traditional methods.

Sometimes I get the feeling that a huge revolution in AI (and to a lesser extent, robotics) is just around the corner. That I am going to wake up one day and there will be some historic event that really catches everyone by surprise. But just as often, it seems like the promise of radical progress due to AI is always just beyond the horizon, and it feels like nothing has really changed over the last few decades.

Certainly to some extent, Watson winning at Jeopardy (and Deep Blue at chess, back in the day) was impressive and got a lot of attention, but it sort of feels like nothing really happened afterwards. That Jeopardy match was over 3 years ago. Since then I've been hearing about how Watson might be useful for this or that, but I haven't actually heard of any results.

So this latest story is cool, and it would be great if Watson could make a big difference, but I get the feeling that it will be at least a couple of years before we hear of any results, and they will probably be along the lines of "Watson seems to have helped a bit (probably!)".

The huge leap in AI happens when we set systems like Watson on the task of developing more complex systems than themselves. This leads to exponential growth. Then it's just a few generations until we have the design for Earth. We'll just need the Magratheans to build it for us.

As for Watson not doing anything useful since Jeopardy, you must have missed this important work.

Watson's text analysis abilities were set loose on the NIH's PubMed database, which contains abstracts of nearly every paper published in peer-reviewed biomedical journals.

Over time, Watson will develop its own sense of what sources it looks at are consistently reliable. Royyuru told Ars that, if the team decides to, it can start adding the full text of articles and branch out to other information sources. Between the known pathways and the scientific literature, however, IBM seems to think that Watson has a good grip on what typically goes on inside cells.

Wow, this reads like something out of an early Asimov story. I'm seriously impressed.

It actually reads a bit like how my managers manager explains to the executive committee a simplified summary of what software I have written does. I'm sure it is so much more complicated than that and they haven't discussed any error rates yet. I'd love to see some information on how they are getting data into the system and how the system is properly classifying the data.

This Watson program is designed to solve a huge problem with medicine right now

We have too much information

The number of publications and information doctors have to go through on a nearly daily basis is astounding. Stuff is being published all over the world in staggering amounts. Even if you're just trying to keep up in a single niche field it's really hard to be 'on the cutting edge' without spending literally every waking moment reading publications. Even the best doctors may not have fully read some paper about a new treatment. Or maybe they missed or forgot something. The point of Watson is to provide all relevant information, and then let the doctor look through it and make a decision. Such that say Watson provides a 'Top 10' list. A doctor might know all treatments except like 3 and 7 or such due to a gap in their knowledge. These knowledge systems are an extension to existing things like drug-drug interaction databases where doctors cannot begin to know all the possible interactions for everything. They're extending these knowledge systems into more diagnosic areas now.

We're not going to get a cure for cancer. Nor are we going to replace doctors entirely

This should lead to at least better diagnosis and treatment options for patients no matter how it all pans out.

dnjake : IBM Research is, oddly enough a pure, research organisation, with actual scientists working on actual science, i.e., a good number of them are spending time working in labs, and Life Sciences makes up a big part of that right now. So you might be surprised by how much these IBM Executives actually know about it.

IBM heads the BOINC distributed computing program which a lot of Universities (and distributed computing groups in general) use to get tons of processing power to grind through data and figure out cures for cancer, aids, malaria, etc.

Once done, the work is sent back for university staff to sift through.

It'd be interesting if Watson became so adept at Bioinformatics, physiology, etc that the distributed computing results could get piped directly into him / her / it (or a similar system modelled after Watson), and save the researchers time of having to sift through the middle-layer end results.

This Watson program is designed to solve a huge problem with medicine right now

We have too much information

The number of publications and information doctors have to go through on a nearly daily basis is astounding. Stuff is being published all over the world in staggering amounts. Even if you're just trying to keep up in a single niche field it's really hard to be 'on the cutting edge' without spending literally every waking moment reading publications. Even the best doctors may not have fully read some paper about a new treatment. Or maybe they missed or forgot something. The point of Watson is to provide all relevant information, and then let the doctor look through it and make a decision. Such that say Watson provides a 'Top 10' list. A doctor might know all treatments except like 3 and 7 or such due to a gap in their knowledge. These knowledge systems are an extension to existing things like drug-drug interaction databases where doctors cannot begin to know all the possible interactions for everything. They're extending these knowledge systems into more diagnosic areas now.

We're not going to get a cure for cancer. Nor are we going to replace doctors entirely

This should lead to at least better diagnosis and treatment options for patients no matter how it all pans out.

If they could teach Watson the fundamentals ... then he could get replicated to other systems. Currently it takes years of medical school & work experience for doctors to pass on knowledge. But, if Watson learns something, he can get cloned.

Watson #1 works on learning the basics of bioinformatics, human physiology, how cancer works.

Watson #2 can get built and cloned with that foundation. Then both Watson #1 & #2 could work on their own specialized things (perhaps one tackling bone cancer and another tackling brain tumors).

Each of those in turn, once they discover enough, could get cloned ... the next Watsons could specialize even further.

Cloning the Watson system would probably take less time and money than sending a student through medical school. And the knowledge each "Watson" learns can get brought back into the fold quicker than doctors having Continuing Medical Educaiton seminars.

Watson's text analysis abilities were set loose on the NIH's PubMed database, which contains abstracts of nearly every paper published in peer-reviewed biomedical journals.

Over time, Watson will develop its own sense of what sources it looks at are consistently reliable. Royyuru told Ars that, if the team decides to, it can start adding the full text of articles and branch out to other information sources. Between the known pathways and the scientific literature, however, IBM seems to think that Watson has a good grip on what typically goes on inside cells.

Wow, this reads like something out of an early Asimov story. I'm seriously impressed.

Sometimes I get the feeling that a huge revolution in AI (and to a lesser extent, robotics) is just around the corner. That I am going to wake up one day and there will be some historic event that really catches everyone by surprise. But just as often, it seems like the promise of radical progress due to AI is always just beyond the horizon, and it feels like nothing has really changed over the last few decades.

Certainly to some extent, Watson winning at Jeopardy (and Deep Blue at chess, back in the day) was impressive and got a lot of attention, but it sort of feels like nothing really happened afterwards. That Jeopardy match was over 3 years ago. Since then I've been hearing about how Watson might be useful for this or that, but I haven't actually heard of any results.

So this latest story is cool, and it would be great if Watson could make a big difference, but I get the feeling that it will be at least a couple of years before we hear of any results, and they will probably be along the lines of "Watson seems to have helped a bit (probably!)".

I just wish that one day I will be browsing Google News and the top story will be "Super AI cures cancer!" or something. I know that is probably too simplistic but I feel like it has been a long time since technology has really impressed me.

Since around 2000 I feel like all of the major progress has been in technology I don't care about, like social networks and smartphones. So please, Watson, do something inspiring already!

But I guess there's at least VR (Oculus Rift) to look forward to!

EDIT: Maybe this is a naive view, but I am still hoping that in the case of AI, there is some threshold that once passed will usher in some really incredible results. AI has seemed disappointing (by the public at least) for decades now, but I hold out hope that it will soon get just good enough to really impress. Referring again to VR, a lot of effort has gone into that field for decades, but only recently with the Oculus Rift has it gotten good enough to be considered "revolutionary". I guess I've just been hoping that Watson is at that same level ("wow, it won at Jeopardy!") but maybe it's not quite there yet.

I forgot to add, I think autonomous cars are also extremely promising. I am hopeful that we will see a revolutionary consumer version within the next 5 years.

Keep in mind that what we consider to be "really good AI" has changed a lot over the years. There's definitely some goal post moving that occurs when it comes considering what is "real artificial intelligence" and what is "just programming".

EDIT: Maybe this is a naive view, but I am still hoping that in the case of AI, there is some threshold that once passed will usher in some really incredible results. AI has seemed disappointing (by the public at least) for decades now, but I hold out hope that it will soon get just good enough to really impress. Referring again to VR, a lot of effort has gone into that field for decades, but only recently with the Oculus Rift has it gotten good enough to be considered "revolutionary". I guess I've just been hoping that Watson is at that same level ("wow, it won at Jeopardy!") but maybe it's not quite there yet.

This reminded me of days spent playing a crazy Russian scientist on a planet four light years away.

Quote:

There are two kinds of scientific progress: the methodical experimentation and categorization which gradually extend the boundaries of knowledge, and the revolutionary leap of genius which redefines and transcends those boundaries. Acknowledging our debt to the former, we yearn, nonetheless, for the latter.

-Academician Prokhor Zakharov, "Address to the Faculty"

Awesome, thanks for this! The quotes were literally my favorite part of that game, and Zakharov's were easily my favorite.

Watson's text analysis abilities were set loose on the NIH's PubMed database, which contains abstracts of nearly every paper published in peer-reviewed biomedical journals.

Over time, Watson will develop its own sense of what sources it looks at are consistently reliable. Royyuru told Ars that, if the team decides to, it can start adding the full text of articles and branch out to other information sources. Between the known pathways and the scientific literature, however, IBM seems to think that Watson has a good grip on what typically goes on inside cells.

Wow, this reads like something out of an early Asimov story. I'm seriously impressed.